Introduction
Hello! I'm Shubham Dubey, a passionate Data Engineer with a passion for transforming complex data into valuable business insights. My journey in the world of data started in India, where I completed my bachelor's in information technology at Savitribai Phule Pune University. Eager to expand my horizons, I moved to the United States to pursue a master's in computer science at the New Jersey Institute of Technology.
Over the past four years, I've had the privilege of working with leading organizations like Pfizer, Covetrus, and NJIT. What excites me most about my work is the opportunity to revolutionize how businesses handle and leverage their data. Each day brings new challenges and opportunities to innovate, whether it's optimizing data processes, troubleshooting complex issues, or implementing robust security measures.
In my current role at Covetrus, I wear many hats. My days are diverse and dynamic, filled with tasks that range from enhancing data management systems to improving data accessibility for various teams. It's this variety and the constant problem-solving that keeps me engaged and passionate about my work.
One of the most rewarding aspects of my career has been witnessing the tangible impact of my efforts. There's an indescribable satisfaction in seeing how the solutions we develop enhance operational efficiency and support strategic decision-making across the organization. It's not just about managing data; it's about empowering businesses to make informed decisions and drive growth.
Beyond my day-to-day responsibilities, I'm proud to have been recognized as a hackathon champion, winning the Facebook F8 Hackathon in the Wit.ai Track in June 2021. This experience not only showcased my ability to innovate under pressure but also reinforced my passion for pushing the boundaries of what's possible with data.
As I continue to grow in my career, I'm excited about the evolving landscape of data engineering and the opportunities it presents. The field is constantly changing, with new technologies and methodologies emerging all the time. Whether it's exploring cloud technologies, diving into machine learning applications, or finding innovative ways to make data more accessible and actionable, I'm always eager to learn and contribute to this dynamic field.
As I move forward in my career, I'm excited to learn more about stream processing with Apache Flink and to explore data mesh architecture. I'm working on a personal project where I analyze climate data, combining my love for the environment with data engineering. My goals include contributing to open-source tools and speaking at a data conference. I also want to improve my skills in ML Ops to connect data engineering with machine learning deployment. The ever-changing nature of our field motivates me to always find new ways to make data easier to use and more effective in solving real-world problems.
My goal is to continue leveraging my expertise to build robust data architectures that empower teams and transform businesses. In this data-driven world, I believe that the right insights at the right time can make all the difference.
Data Science Fellow
career options
The field of data engineering offers a diverse range of career paths for those passionate about working with data at scale. These roles are crucial in today's data-driven world, spanning from building robust data infrastructures to developing advanced analytics solutions. Each position requires a unique set of skills and plays a vital role in helping organizations leverage their data assets effectively.
Data Science Fellow
skills
What are the main hard skills you use on a daily basis in your current job?
As a data engineer, I frequently design and implement data pipelines to efficiently move and transform data from various sources to destinations. This involves using tools like Apache Kafka for real-time data streaming and Apache Airflow for workflow orchestration. I learned these skills through a combination of on-the-job training and online courses. In practice, I use these skills to ensure smooth data flow for analytics and reporting teams, enabling them to access up-to-date information for business intelligence.
Proficiency in SQL is crucial for querying, manipulating, and managing data in relational databases. I use SQL daily to extract data, create and optimize complex queries, and maintain data integrity. I initially learned SQL during my university courses and have continuously enhanced my skills through practical application. This skill is essential when working with data warehouses, performing data transformations, and supporting data analysts in their reporting needs.
With the increasing adoption of cloud services, expertise in cloud platforms like AWS, Google Cloud, or Azure is vital. I work extensively with cloud-based data storage and processing services, such as Amazon S3 for storage and Amazon Redshift for data warehousing. Data warehousing involves collecting and managing data from various sources into a centralized repository, optimized for analytics and business intelligence. I acquired these skills through certification courses and hands-on projects. In my daily work, I leverage these technologies to build scalable and cost-effective data solutions, ensuring our data infrastructure can handle growing volumes of data.
Python is my go-to language for data manipulation, ETL Extract Transformation and Load data processes, and automating data workflows. I use libraries like Pandas for data analysis and PySpark for big data processing. I started learning Python during my master's program and have continually improved through practical applications and online resources. This skill is crucial for developing custom data processing scripts, implementing data quality checks, and creating data transformation logic.
Proficiency in Git for version control and collaboration platforms like GitHub or GitLab is essential in my role. Version control is a system that tracks changes to files over time, allowing multiple people to work on projects simultaneously without conflicts. These tools allow me to manage code changes, collaborate with team members, and maintain a history of our data pipeline developments. I learned these skills through a combination of online tutorials and practical use in team projects. Daily, I use these tools to track changes in our data infrastructure code, collaborate on new features, and manage deployments of our data solutions.
What are the main soft skills you use on a daily basis in your current job?
As a data engineer, I constantly apply analytical thinking to design efficient data architectures and solve complex data integration challenges. I've developed this skill through tackling diverse projects and continuously questioning how to optimize our data systems. For instance, when faced with performance issues in our data pipelines, I break down the problem systematically, analyzing each component to identify bottlenecks and implement targeted improvements.
Effectively managing expectations and requirements of various stakeholders is crucial in my role. I've honed this skill by actively engaging with different departments, from marketing teams needing specific data insights to C-level executives requiring high-level data summaries. This involves understanding their unique needs, translating technical concepts into business value, and negotiating realistic timelines for data deliverables.
The rapid evolution of data technologies necessitates a commitment to ongoing learning. I've cultivated this skill by allocating time each week to explore new tools, attend webinars, and experiment with emerging data engineering techniques. This proactive approach to learning has enabled me to introduce innovative solutions, such as implementing streaming data processing to replace batch jobs, significantly improving our real-time analytics capabilities.
Data engineering often involves dealing with unexpected challenges, from data inconsistencies to system failures. I've developed resilience by maintaining a positive attitude in the face of setbacks and viewing them as opportunities for improvement. This mindset has been particularly valuable when managing production issues, allowing me to stay calm under pressure and methodically work towards solutions.
Although not in a formal leadership role, I often take the lead on cross-functional data initiatives. I've developed this skill by volunteering to coordinate projects that span multiple teams, such as data governance implementations or company-wide data quality initiatives. This involves aligning different perspectives, facilitating decision-making, and ensuring all teams are working cohesively towards shared data goals.
Shubham
’s personal path
Tell us about your personal journey in
Data Science Fellow
:
My journey into data engineering started with a simple curiosity. I can still clearly remember the first time I wrote a "Hello, World!" program in C++ back in high school. It felt like magic, and I was immediately captivated. That experience sparked my interest in computer science, driving me to learn more about coding and technology.
In the early days of my career, I began as a backend software developer. While I enjoyed the technical challenges, I wanted to see the immediate impact of my work. This led me to explore data and analytics during my master’s program, where I got involved in machine learning and data analysis through various projects.
A turning point for me was working with Pfizer’s analytical team on optimizing the COVID-19 vaccine supply chain. Using geospatial data to improve this crucial process showed me the real-world impact of data-driven solutions. It was thrilling to see how my work could make a difference, possibly affecting millions of lives.
Finding a job wasn’t easy. I applied to many positions, faced several rejections, and had moments of doubt. But each interview, whether I got the job or not, taught me something new about the industry and myself. I used platforms like LinkedIn and GitHub to showcase my projects and connect with professionals in the field.
One of the toughest challenges was turning my academic projects into industry-relevant skills. I tackled this by working on personal projects that mirrored real-world data engineering tasks and contributing to open-source projects. This not only improved my skills but also showed potential employers that I took the initiative.
The interview process for my current role was tough, with multiple rounds of technical interviews and a challenging take-home project. It tested not only my technical skills but also my problem-solving abilities and how well I could explain complex ideas. This experience taught me the importance of understanding not just the 'how' but also the 'why' behind data engineering principles.
Looking back, I realize that getting my dream job wasn’t about one big moment of success, but rather a series of small victories, lessons, and persistent effort.
What would you tell your younger you regarding building your current career?
Keep learning continuously. Prioritize understanding core principles instead of just chasing the latest trends.
When I was just starting out, one thing I wish I had known was the importance of continuous learning. It’s easy to get caught up in chasing the latest trends, but trust me, understanding the core principles will take you much further. I remember when I started contributing to open-source projects—it wasn’t easy, but it gave me invaluable hands-on experience. Building a network is something I didn’t prioritize early enough, and looking back, I realize how crucial those connections are. Also, don’t overlook soft skills; they’ll serve you just as well as your technical abilities. Your career journey might not be a straight line—mine certainly wasn’t—but every twist and turn adds depth to your perspective. Challenges will come but stay resilient. You’ve got this!
Final thoughts & tips
Data engineering is more than just building pipelines – it’s about solving real-world problems and driving innovation. Early in my career, we struggled to monitor multiple data streams effectively. I proposed a Tableau dashboard to provide real-time visibility across all pipelines, which allowed us to catch issues early and improve decision-making. This experience taught me that managing and monitoring data pipelines is just as critical as building them. The most impactful data engineers turn raw data into actionable insights. So, think big, be bold, and let your creativity guide you in transforming businesses.
Resources to dig in more
Data Engineering Weekly
A newsletter that delivers the latest trends, tools, and insights in data engineering directly to your inbox.
The Data Engineering Podcast
This podcast interviews data engineering professionals, discussing real-world challenges and solutions.
Awesome Data Engineering
A comprehensive GitHub repository that compiles a list of data engineering tools, frameworks, and resources.
Data Engineering Practice Problems
This GitHub repository offers a collection of real-world data engineering problems and projects.
